The goal of class-incremental online continual learning [1]} focuses on how an artificially intelligent agent learns to classify new classes without forgetting its knowledge on the classes previously learnt (where such an issue is the so-called catastrophic forgetting). To tackle against the catastrophic forgetting, one should balance the model learning between the old classes and new classes, where the literature roughly contains three branches:
